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This paper presents a coded aperture method to remotely estimate the radioactivity of a source. The activity is estimated from the detected counts and the estimated source location, which is extracted by factoring the effect of aperture magnification. A 6mm thick tungsten-copper alloy coded aperture mask is used to modulate the incoming gamma-rays. The location of point and line sources in all three dimensions was estimated with an accuracy of less than 10% when the source-camera distance was about 4 m. The estimated activities were 17.6% smaller and 50.4% larger than the actual activities for the point and line sources, respectively.

We develop a compact, high signal-to-noise ratio line-detector array Compton scatter imaging system based on Y2SiO5 scintillators and silicon photomultipliers. It is shown that the system's response uniformity is better than 98% for low-Zeff materials after a specific correction. Backscatter images of an “IHEP”-character model, cubes of different materials, and some other applications are presented, to show its potential for use in nondestructive testing.

The paper presents a deterministic model, which takes into account the final water storage and the effects of water head, for optimal mid-long term generation scheduling of a hydrothermal system. Considering its large scale mixed integer nonlinear structure, a hybrid algorithm based on Genetic Algorithm and decomposition algorithm is developed to solve it. The model is divided into thermal power dispatch and hydro power dispatch subproblems respectively by using reservoir volumes as genetic variables which consider the complex relationship between reservoirs. These two subproblems are independently solved according to their own characteristics. The simulation results reveal that comparing with GA the hybrid algorithm is an effective method with shorter computation time and higher precision.

The large scale penetration of Distributed Generation (DG) within distribution networks will significantly impact on the operation of future distribution systems. It will also be possible to take advantage of DG with regard to the service restoration in distribution networks. For example, when there is not enough capacity to restore the entire out-of-service area, the network operator can implement intentional island operation with the support of black-start DG. Furthermore, from the viewpoint of making full use of renewable energy sources, the network operator is required to ensure the interconnection of the renewable units that are installed in the out-of-service area as many as possible. Based on these assumptions, this paper establishes a new optimization model for the solution of the service restoration problem that incorporates DG and applies NSGA-II to solve the model. Numerical analysis verifies the feasibility and correctness of the model.

Distribution maintenance time scheduling is a multi-objects and multi-constrains optimizing problem. To solve this complicated problem, this paper presents an improved genetic algorithm that developed "direct compare algorithm" in constructing fitness function and mutation operator of simple genetic algorithm. By introducing "infeasible area", the improved algorithm allows individuals among the infeasible area compete with their target values. So a part of infeasible individuals with good gene can be reserved and the performance of the optimizing algorithm has been improved. At the same time, the improved algorithm designs a new mutation operator which mutates by tabu searching when the population is multiplex enough and changes its mutates way to uniform automatically when the average population multiplex lower down to a fix degree. The tabu searching mutation operator can speed up constringency of the algorithm and the uniform mutation operator can bring new gene to avoid "premature" of genetic algorithm. The proposed method is applied to a practical system, and numerical results verify the correctness and validity of it.

Based on the analysis of the portfolio distribution problem of renewable energy additional price, this paper drew three feasible rules from the practical work and established the optimal model for it, which intended to achieve such goals that decrease the trade counts, ensure the priority of province grid companies among the same area grid company and keep the trade sides stable without violating the capital restrictions of both buy and sell side. This paper adopted genetic algorithm to solve this problem, but when applied penalty function to deal with equality constraints, it often performance bad. So this paper introduced heuristic rules to amend the infeasible individuals which generated during the calculation of genetic algorithm. The proposed model and method are applied to a test system and numerical results verify the correctness and validity of them.

Positron Emission Mammography imaging system (PEMi) is a nuclear medicine diagnosis method dedicated for breast imaging. It provides a better resolution in detection of millimeter-sized breast tumors than whole-body PET. To address the requirement of semi-quantitative analysis with the radiotracer concentration map of the breast, a new attenuation correction method based on three-dimensional seeded region growing image segmentation (3DSRG-AC) solution was developed. The method gives a 3D connected region as the segmentation result instead of image slices. The continuously segmentation property makes this new method free of activity variation of breast tissues. Threshold value chosen is the key point for the segmentation process. The first valley of the grey level histogram of the reconstruction image is set as the lower threshold, which works fine in clinical application. Results show that attenuation correction for PEMi improves the image quality and the quantitative accuracy of radioactivity distribution determination. Attenuation correction also improves the probabilities to detect small and early breast tumors.